Overview

Dataset statistics

Number of variables26
Number of observations30172
Missing cells55821
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 MiB
Average record size in memory208.0 B

Variable types

Numeric19
Categorical7

Alerts

time has a high cardinality: 4931 distinct valuesHigh cardinality
rally is highly overall correlated with roundscore_A and 1 other fieldsHigh correlation
ball_round is highly overall correlated with rally_lengthHigh correlation
frame_num is highly overall correlated with setHigh correlation
roundscore_A is highly overall correlated with rally and 1 other fieldsHigh correlation
roundscore_B is highly overall correlated with rally and 1 other fieldsHigh correlation
player is highly overall correlated with getpoint_playerHigh correlation
landing_area is highly overall correlated with landing_height and 1 other fieldsHigh correlation
landing_y is highly overall correlated with player_location_y and 2 other fieldsHigh correlation
getpoint_player is highly overall correlated with player and 1 other fieldsHigh correlation
player_location_x is highly overall correlated with opponent_location_xHigh correlation
player_location_y is highly overall correlated with landing_y and 1 other fieldsHigh correlation
opponent_location_x is highly overall correlated with player_location_xHigh correlation
opponent_location_y is highly overall correlated with landing_y and 1 other fieldsHigh correlation
match_id is highly overall correlated with rally_idHigh correlation
rally_id is highly overall correlated with match_idHigh correlation
rally_length is highly overall correlated with ball_roundHigh correlation
type is highly overall correlated with backhand and 1 other fieldsHigh correlation
backhand is highly overall correlated with typeHigh correlation
landing_height is highly overall correlated with landing_area and 4 other fieldsHigh correlation
lose_reason is highly overall correlated with landing_area and 1 other fieldsHigh correlation
set is highly overall correlated with frame_numHigh correlation
aroundhead is highly imbalanced (53.2%)Imbalance
lose_reason has 27908 (92.5%) missing valuesMissing
getpoint_player has 27908 (92.5%) missing valuesMissing
roundscore_A has 647 (2.1%) zerosZeros
roundscore_B has 1784 (5.9%) zerosZeros
player has 875 (2.9%) zerosZeros

Reproduction

Analysis started2023-04-20 07:36:51.295194
Analysis finished2023-04-20 07:38:45.598175
Duration1 minute and 54.3 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

rally
Real number (ℝ)

Distinct48
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.699755
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:45.799157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median19
Q328
95-th percentile37
Maximum50
Range49
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.178057
Coefficient of variation (CV)0.5977649
Kurtosis-0.98126993
Mean18.699755
Median Absolute Deviation (MAD)9
Skewness0.15282622
Sum564209
Variance124.94896
MonotonicityNot monotonic
2023-04-20T07:38:46.134382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 1159
 
3.8%
2 1034
 
3.4%
21 955
 
3.2%
14 945
 
3.1%
28 927
 
3.1%
19 921
 
3.1%
23 910
 
3.0%
8 904
 
3.0%
4 898
 
3.0%
3 896
 
3.0%
Other values (38) 20623
68.4%
ValueCountFrequency (%)
1 1159
3.8%
2 1034
3.4%
3 896
3.0%
4 898
3.0%
5 734
2.4%
6 787
2.6%
7 691
2.3%
8 904
3.0%
9 774
2.6%
10 781
2.6%
ValueCountFrequency (%)
50 22
 
0.1%
48 16
 
0.1%
47 8
 
< 0.1%
45 46
 
0.2%
44 39
 
0.1%
43 57
 
0.2%
42 128
0.4%
41 164
0.5%
40 204
0.7%
39 257
0.9%

ball_round
Real number (ℝ)

Distinct70
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8242742
Minimum1
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:46.425670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q313
95-th percentile27
Maximum70
Range69
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.4465505
Coefficient of variation (CV)0.85976331
Kurtosis4.5515956
Mean9.8242742
Median Absolute Deviation (MAD)4
Skewness1.8049896
Sum296418
Variance71.344215
MonotonicityNot monotonic
2023-04-20T07:38:46.715194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2268
 
7.5%
2 2268
 
7.5%
3 2268
 
7.5%
4 2268
 
7.5%
5 2268
 
7.5%
6 2037
 
6.8%
7 1844
 
6.1%
8 1658
 
5.5%
9 1492
 
4.9%
10 1309
 
4.3%
Other values (60) 10492
34.8%
ValueCountFrequency (%)
1 2268
7.5%
2 2268
7.5%
3 2268
7.5%
4 2268
7.5%
5 2268
7.5%
6 2037
6.8%
7 1844
6.1%
8 1658
5.5%
9 1492
4.9%
10 1309
4.3%
ValueCountFrequency (%)
70 1
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
66 1
 
< 0.1%
65 2
< 0.1%
64 2
< 0.1%
63 2
< 0.1%
62 2
< 0.1%
61 3
< 0.1%

time
Categorical

Distinct4931
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size235.8 KiB
00:20:12
 
21
00:41:29
 
19
00:20:09
 
19
00:30:43
 
19
00:23:53
 
18
Other values (4926)
30076 

Length

Max length8
Median length8
Mean length7.9849861
Min length7

Characters and Unicode

Total characters240923
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique878 ?
Unique (%)2.9%

Sample

1st row00:06:00
2nd row00:06:01
3rd row00:06:02
4th row00:06:03
5th row00:06:04

Common Values

ValueCountFrequency (%)
00:20:12 21
 
0.1%
00:41:29 19
 
0.1%
00:20:09 19
 
0.1%
00:30:43 19
 
0.1%
00:23:53 18
 
0.1%
00:34:22 18
 
0.1%
00:41:31 18
 
0.1%
00:20:13 18
 
0.1%
00:20:10 18
 
0.1%
00:11:13 18
 
0.1%
Other values (4921) 29986
99.4%

Length

2023-04-20T07:38:46.989045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:20:12 21
 
0.1%
00:30:43 19
 
0.1%
00:41:29 19
 
0.1%
00:20:09 19
 
0.1%
00:34:22 18
 
0.1%
00:41:31 18
 
0.1%
00:20:13 18
 
0.1%
00:20:10 18
 
0.1%
00:11:13 18
 
0.1%
00:11:12 18
 
0.1%
Other values (4921) 29986
99.4%

Most occurring characters

ValueCountFrequency (%)
0 71849
29.8%
: 60344
25.0%
1 20503
 
8.5%
2 17036
 
7.1%
3 16610
 
6.9%
4 16413
 
6.8%
5 14266
 
5.9%
9 6190
 
2.6%
8 6051
 
2.5%
7 5971
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180579
75.0%
Other Punctuation 60344
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 71849
39.8%
1 20503
 
11.4%
2 17036
 
9.4%
3 16610
 
9.2%
4 16413
 
9.1%
5 14266
 
7.9%
9 6190
 
3.4%
8 6051
 
3.4%
7 5971
 
3.3%
6 5690
 
3.2%
Other Punctuation
ValueCountFrequency (%)
: 60344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 240923
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 71849
29.8%
: 60344
25.0%
1 20503
 
8.5%
2 17036
 
7.1%
3 16610
 
6.9%
4 16413
 
6.8%
5 14266
 
5.9%
9 6190
 
2.6%
8 6051
 
2.5%
7 5971
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 240923
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 71849
29.8%
: 60344
25.0%
1 20503
 
8.5%
2 17036
 
7.1%
3 16610
 
6.9%
4 16413
 
6.8%
5 14266
 
5.9%
9 6190
 
2.6%
8 6051
 
2.5%
7 5971
 
2.5%

frame_num
Real number (ℝ)

Distinct26536
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63014.843
Minimum186
Maximum179169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:47.249740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum186
5-th percentile16727.55
Q135976
median60708
Q385646.5
95-th percentile121138.9
Maximum179169
Range178983
Interquartile range (IQR)49670.5

Descriptive statistics

Standard deviation32797.553
Coefficient of variation (CV)0.52047345
Kurtosis-0.35310723
Mean63014.843
Median Absolute Deviation (MAD)24834.5
Skewness0.449498
Sum1.9012838 × 109
Variance1.0756795 × 109
MonotonicityNot monotonic
2023-04-20T07:38:47.540840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37603 5
 
< 0.1%
76043 4
 
< 0.1%
36598 4
 
< 0.1%
54986 4
 
< 0.1%
55160 4
 
< 0.1%
38087 4
 
< 0.1%
80904 4
 
< 0.1%
58089 4
 
< 0.1%
43099 4
 
< 0.1%
12802 4
 
< 0.1%
Other values (26526) 30131
99.9%
ValueCountFrequency (%)
186 1
< 0.1%
244 1
< 0.1%
264 1
< 0.1%
299 1
< 0.1%
331 1
< 0.1%
366 1
< 0.1%
394 1
< 0.1%
424 1
< 0.1%
480 1
< 0.1%
493 1
< 0.1%
ValueCountFrequency (%)
179169 1
< 0.1%
179132 1
< 0.1%
179104 1
< 0.1%
179091 1
< 0.1%
179055 1
< 0.1%
179028 1
< 0.1%
179014 1
< 0.1%
178973 1
< 0.1%
178951 1
< 0.1%
177617 1
< 0.1%

roundscore_A
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.539374
Minimum0
Maximum26
Zeros647
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:47.815369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q316
95-th percentile20
Maximum26
Range26
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.2031452
Coefficient of variation (CV)0.58856864
Kurtosis-1.140455
Mean10.539374
Median Absolute Deviation (MAD)5
Skewness0.0019963032
Sum317994
Variance38.47901
MonotonicityNot monotonic
2023-04-20T07:38:48.088685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 1936
 
6.4%
9 1708
 
5.7%
12 1638
 
5.4%
14 1566
 
5.2%
2 1526
 
5.1%
10 1502
 
5.0%
16 1497
 
5.0%
13 1483
 
4.9%
15 1386
 
4.6%
20 1384
 
4.6%
Other values (16) 14546
48.2%
ValueCountFrequency (%)
0 647
 
2.1%
1 1936
6.4%
2 1526
5.1%
3 1363
4.5%
4 1228
4.1%
5 1334
4.4%
6 1166
3.9%
7 1272
4.2%
8 1292
4.3%
9 1708
5.7%
ValueCountFrequency (%)
26 22
 
0.1%
24 16
 
0.1%
23 67
 
0.2%
22 114
 
0.4%
21 905
3.0%
20 1384
4.6%
19 1294
4.3%
18 1246
4.1%
17 1337
4.4%
16 1497
5.0%

roundscore_B
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1603805
Minimum0
Maximum24
Zeros1784
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:48.323575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q312
95-th percentile18
Maximum24
Range24
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.65198
Coefficient of variation (CV)0.69261232
Kurtosis-0.76136639
Mean8.1603805
Median Absolute Deviation (MAD)4
Skewness0.42532685
Sum246215
Variance31.944878
MonotonicityNot monotonic
2023-04-20T07:38:48.600312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 2117
 
7.0%
4 1964
 
6.5%
2 1949
 
6.5%
6 1919
 
6.4%
7 1906
 
6.3%
3 1895
 
6.3%
0 1784
 
5.9%
5 1738
 
5.8%
8 1726
 
5.7%
9 1504
 
5.0%
Other values (15) 11670
38.7%
ValueCountFrequency (%)
0 1784
5.9%
1 2117
7.0%
2 1949
6.5%
3 1895
6.3%
4 1964
6.5%
5 1738
5.8%
6 1919
6.4%
7 1906
6.3%
8 1726
5.7%
9 1504
5.0%
ValueCountFrequency (%)
24 46
 
0.2%
23 10
 
< 0.1%
22 85
 
0.3%
21 311
 
1.0%
20 401
1.3%
19 544
1.8%
18 715
2.4%
17 942
3.1%
16 762
2.5%
15 999
3.3%

player
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.058796
Minimum0
Maximum34
Zeros875
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:48.850062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median14
Q320
95-th percentile31
Maximum34
Range34
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.9293844
Coefficient of variation (CV)0.63514573
Kurtosis-0.70634605
Mean14.058796
Median Absolute Deviation (MAD)7
Skewness0.41587156
Sum424182
Variance79.733907
MonotonicityNot monotonic
2023-04-20T07:38:49.110524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
10 2957
 
9.8%
2 2609
 
8.6%
16 2416
 
8.0%
5 2383
 
7.9%
14 2300
 
7.6%
13 1428
 
4.7%
15 1290
 
4.3%
11 879
 
2.9%
0 875
 
2.9%
9 859
 
2.8%
Other values (25) 12176
40.4%
ValueCountFrequency (%)
0 875
 
2.9%
1 168
 
0.6%
2 2609
8.6%
3 301
 
1.0%
4 771
 
2.6%
5 2383
7.9%
6 353
 
1.2%
7 518
 
1.7%
8 288
 
1.0%
9 859
 
2.8%
ValueCountFrequency (%)
34 128
 
0.4%
33 331
 
1.1%
32 426
1.4%
31 838
2.8%
30 787
2.6%
29 367
1.2%
28 438
1.5%
27 795
2.6%
26 512
1.7%
25 326
 
1.1%

type
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size235.8 KiB
net shot
5725 
lob
5207 
defensive shot
4087 
smash
3682 
drop
3182 
Other values (5)
8289 

Length

Max length14
Median length12
Mean length7.1842105
Min length3

Characters and Unicode

Total characters216762
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowshort service
2nd rownet shot
3rd rowlob
4th rowclear
5th rowdrop

Common Values

ValueCountFrequency (%)
net shot 5725
19.0%
lob 5207
17.3%
defensive shot 4087
13.5%
smash 3682
12.2%
drop 3182
10.5%
clear 3078
10.2%
push/rush 2011
 
6.7%
short service 1620
 
5.4%
drive 932
 
3.1%
long service 648
 
2.1%

Length

2023-04-20T07:38:49.386662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-20T07:38:49.700422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
shot 9812
23.2%
net 5725
13.5%
lob 5207
12.3%
defensive 4087
9.7%
smash 3682
 
8.7%
drop 3182
 
7.5%
clear 3078
 
7.3%
service 2268
 
5.4%
push/rush 2011
 
4.8%
short 1620
 
3.8%
Other values (2) 1580
 
3.7%

Most occurring characters

ValueCountFrequency (%)
s 29173
13.5%
e 26532
12.2%
o 20469
 
9.4%
h 19136
 
8.8%
t 17157
 
7.9%
r 13091
 
6.0%
12080
 
5.6%
n 10460
 
4.8%
l 8933
 
4.1%
d 8201
 
3.8%
Other values (11) 51530
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 202671
93.5%
Space Separator 12080
 
5.6%
Other Punctuation 2011
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 29173
14.4%
e 26532
13.1%
o 20469
10.1%
h 19136
9.4%
t 17157
8.5%
r 13091
 
6.5%
n 10460
 
5.2%
l 8933
 
4.4%
d 8201
 
4.0%
i 7287
 
3.6%
Other values (9) 42232
20.8%
Space Separator
ValueCountFrequency (%)
12080
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2011
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 202671
93.5%
Common 14091
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 29173
14.4%
e 26532
13.1%
o 20469
10.1%
h 19136
9.4%
t 17157
8.5%
r 13091
 
6.5%
n 10460
 
5.2%
l 8933
 
4.4%
d 8201
 
4.0%
i 7287
 
3.6%
Other values (9) 42232
20.8%
Common
ValueCountFrequency (%)
12080
85.7%
/ 2011
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 216762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 29173
13.5%
e 26532
12.2%
o 20469
 
9.4%
h 19136
 
8.8%
t 17157
 
7.9%
r 13091
 
6.0%
12080
 
5.6%
n 10460
 
4.8%
l 8933
 
4.1%
d 8201
 
3.8%
Other values (11) 51530
23.8%

aroundhead
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size235.8 KiB
0.0
27161 
1.0
3011 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90516
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 27161
90.0%
1.0 3011
 
10.0%

Length

2023-04-20T07:38:49.994410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-20T07:38:50.229094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 27161
90.0%
1.0 3011
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 57333
63.3%
. 30172
33.3%
1 3011
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60344
66.7%
Other Punctuation 30172
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 57333
95.0%
1 3011
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 30172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90516
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 57333
63.3%
. 30172
33.3%
1 3011
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90516
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 57333
63.3%
. 30172
33.3%
1 3011
 
3.3%

backhand
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size235.8 KiB
0
19237 
1
10935 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30172
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 19237
63.8%
1 10935
36.2%

Length

2023-04-20T07:38:50.427323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-20T07:38:50.695243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19237
63.8%
1 10935
36.2%

Most occurring characters

ValueCountFrequency (%)
0 19237
63.8%
1 10935
36.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30172
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19237
63.8%
1 10935
36.2%

Most occurring scripts

ValueCountFrequency (%)
Common 30172
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19237
63.8%
1 10935
36.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19237
63.8%
1 10935
36.2%

landing_height
Categorical

Distinct2
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size235.8 KiB
2.0
18683 
1.0
11484 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90501
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 18683
61.9%
1.0 11484
38.1%
(Missing) 5
 
< 0.1%

Length

2023-04-20T07:38:50.894287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-20T07:38:51.137724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 18683
61.9%
1.0 11484
38.1%

Most occurring characters

ValueCountFrequency (%)
. 30167
33.3%
0 30167
33.3%
2 18683
20.6%
1 11484
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60334
66.7%
Other Punctuation 30167
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30167
50.0%
2 18683
31.0%
1 11484
 
19.0%
Other Punctuation
ValueCountFrequency (%)
. 30167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90501
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 30167
33.3%
0 30167
33.3%
2 18683
20.6%
1 11484
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90501
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 30167
33.3%
0 30167
33.3%
2 18683
20.6%
1 11484
 
12.7%

landing_area
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2927217
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:51.325275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q37
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6208277
Coefficient of variation (CV)0.49517579
Kurtosis-1.1330905
Mean5.2927217
Median Absolute Deviation (MAD)2
Skewness-0.21232139
Sum159692
Variance6.8687376
MonotonicityNot monotonic
2023-04-20T07:38:51.538907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 4899
16.2%
8 4140
13.7%
6 3724
12.3%
5 3537
11.7%
2 3503
11.6%
1 3317
11.0%
9 2229
7.4%
4 2084
6.9%
3 1980
6.6%
10 759
 
2.5%
ValueCountFrequency (%)
1 3317
11.0%
2 3503
11.6%
3 1980
6.6%
4 2084
6.9%
5 3537
11.7%
6 3724
12.3%
7 4899
16.2%
8 4140
13.7%
9 2229
7.4%
10 759
 
2.5%
ValueCountFrequency (%)
10 759
 
2.5%
9 2229
7.4%
8 4140
13.7%
7 4899
16.2%
6 3724
12.3%
5 3537
11.7%
4 2084
6.9%
3 1980
6.6%
2 3503
11.6%
1 3317
11.0%

landing_x
Real number (ℝ)

Distinct2885
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.021232373
Minimum-3.4804878
Maximum3.5231707
Zeros3
Zeros (%)< 0.1%
Negative15254
Negative (%)50.6%
Memory size235.8 KiB
2023-04-20T07:38:51.786835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.4804878
5-th percentile-1.2914634
Q1-0.97317073
median-0.039634146
Q31.047561
95-th percentile1.3804878
Maximum3.5231707
Range7.0036585
Interquartile range (IQR)2.0207317

Descriptive statistics

Standard deviation1.0040084
Coefficient of variation (CV)47.286679
Kurtosis-1.5584608
Mean0.021232373
Median Absolute Deviation (MAD)0.99939024
Skewness0.047850452
Sum640.62317
Variance1.0080329
MonotonicityNot monotonic
2023-04-20T07:38:52.061197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.079268293 45
 
0.1%
1.170731707 39
 
0.1%
-1.106097561 38
 
0.1%
-1.117073171 38
 
0.1%
-1.062195122 38
 
0.1%
-1.101219512 36
 
0.1%
-1.12804878 36
 
0.1%
-1.201219512 34
 
0.1%
1.17804878 34
 
0.1%
1.157317073 34
 
0.1%
Other values (2875) 29800
98.8%
ValueCountFrequency (%)
-3.480487805 1
< 0.1%
-3.331707317 1
< 0.1%
-3.086585366 1
< 0.1%
-2.91097561 1
< 0.1%
-2.643902439 1
< 0.1%
-2.579268293 1
< 0.1%
-2.446341463 1
< 0.1%
-2.42804878 1
< 0.1%
-2.420731707 1
< 0.1%
-2.342682927 1
< 0.1%
ValueCountFrequency (%)
3.523170732 1
< 0.1%
3.508536585 1
< 0.1%
3.263414634 1
< 0.1%
3.151219512 1
< 0.1%
3.020731707 1
< 0.1%
2.668292683 1
< 0.1%
2.647560976 1
< 0.1%
2.636585366 1
< 0.1%
2.434146341 1
< 0.1%
2.320731707 1
< 0.1%

landing_y
Real number (ℝ)

Distinct23074
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.045165777
Minimum-1.9617708
Maximum1.953125
Zeros0
Zeros (%)0.0%
Negative14676
Negative (%)48.6%
Memory size235.8 KiB
2023-04-20T07:38:52.354863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.9617708
5-th percentile-1.4856771
Q1-0.84451823
median0.12229167
Q30.91919271
95-th percentile1.5918984
Maximum1.953125
Range3.9148958
Interquartile range (IQR)1.7637109

Descriptive statistics

Standard deviation1.0141633
Coefficient of variation (CV)22.454243
Kurtosis-1.1901781
Mean0.045165777
Median Absolute Deviation (MAD)0.86333333
Skewness-0.0071113345
Sum1362.7418
Variance1.0285273
MonotonicityNot monotonic
2023-04-20T07:38:52.661268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.40828125 7
 
< 0.1%
0.4936458333 7
 
< 0.1%
1.418020833 6
 
< 0.1%
-1.39390625 6
 
< 0.1%
-0.9505208333 6
 
< 0.1%
-1.452552083 6
 
< 0.1%
-1.408333333 6
 
< 0.1%
1.495416667 6
 
< 0.1%
-1.021354167 6
 
< 0.1%
-1.525989583 5
 
< 0.1%
Other values (23064) 30111
99.8%
ValueCountFrequency (%)
-1.961770833 1
< 0.1%
-1.911927083 1
< 0.1%
-1.8996875 1
< 0.1%
-1.8890625 1
< 0.1%
-1.88546875 1
< 0.1%
-1.8625 1
< 0.1%
-1.86 1
< 0.1%
-1.857604167 1
< 0.1%
-1.853697917 1
< 0.1%
-1.851979167 1
< 0.1%
ValueCountFrequency (%)
1.953125 1
< 0.1%
1.93890625 1
< 0.1%
1.927760417 1
< 0.1%
1.89578125 1
< 0.1%
1.893333333 1
< 0.1%
1.88703125 1
< 0.1%
1.883489583 1
< 0.1%
1.879791667 1
< 0.1%
1.873177083 1
< 0.1%
1.871197917 1
< 0.1%

lose_reason
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.2%
Missing27908
Missing (%)92.5%
Memory size235.8 KiB
opponent's ball landed
701 
out
687 
touched the net
533 
not pass over the net
262 
misjudged
81 

Length

Max length22
Median length21
Mean length14.005742
Min length3

Characters and Unicode

Total characters31709
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowout
2nd rowtouched the net
3rd rowout
4th rowtouched the net
5th rownot pass over the net

Common Values

ValueCountFrequency (%)
opponent's ball landed 701
 
2.3%
out 687
 
2.3%
touched the net 533
 
1.8%
not pass over the net 262
 
0.9%
misjudged 81
 
0.3%
(Missing) 27908
92.5%

Length

2023-04-20T07:38:53.637045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-20T07:38:53.914474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
the 795
13.8%
net 795
13.8%
opponent's 701
12.1%
ball 701
12.1%
landed 701
12.1%
out 687
11.9%
touched 533
9.2%
not 262
 
4.5%
pass 262
 
4.5%
over 262
 
4.5%

Most occurring characters

ValueCountFrequency (%)
e 3868
12.2%
t 3773
11.9%
3516
11.1%
n 3160
10.0%
o 3146
9.9%
l 2103
 
6.6%
d 2097
 
6.6%
a 1664
 
5.2%
p 1664
 
5.2%
h 1328
 
4.2%
Other values (11) 5390
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27492
86.7%
Space Separator 3516
 
11.1%
Other Punctuation 701
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3868
14.1%
t 3773
13.7%
n 3160
11.5%
o 3146
11.4%
l 2103
7.6%
d 2097
7.6%
a 1664
6.1%
p 1664
6.1%
h 1328
 
4.8%
s 1306
 
4.8%
Other values (9) 3383
12.3%
Space Separator
ValueCountFrequency (%)
3516
100.0%
Other Punctuation
ValueCountFrequency (%)
' 701
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27492
86.7%
Common 4217
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3868
14.1%
t 3773
13.7%
n 3160
11.5%
o 3146
11.4%
l 2103
7.6%
d 2097
7.6%
a 1664
6.1%
p 1664
6.1%
h 1328
 
4.8%
s 1306
 
4.8%
Other values (9) 3383
12.3%
Common
ValueCountFrequency (%)
3516
83.4%
' 701
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3868
12.2%
t 3773
11.9%
3516
11.1%
n 3160
10.0%
o 3146
9.9%
l 2103
 
6.6%
d 2097
 
6.6%
a 1664
 
5.2%
p 1664
 
5.2%
h 1328
 
4.2%
Other values (11) 5390
17.0%

getpoint_player
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)1.0%
Missing27908
Missing (%)92.5%
Infinite0
Infinite (%)0.0%
Mean13.430212
Minimum0
Maximum32
Zeros83
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:54.144102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median13
Q320
95-th percentile30
Maximum32
Range32
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8101941
Coefficient of variation (CV)0.65599814
Kurtosis-0.73650476
Mean13.430212
Median Absolute Deviation (MAD)7
Skewness0.40501484
Sum30406
Variance77.61952
MonotonicityNot monotonic
2023-04-20T07:38:54.369183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
10 323
 
1.1%
14 273
 
0.9%
2 260
 
0.9%
5 156
 
0.5%
27 134
 
0.4%
21 120
 
0.4%
13 100
 
0.3%
20 90
 
0.3%
0 83
 
0.3%
32 78
 
0.3%
Other values (12) 647
 
2.1%
(Missing) 27908
92.5%
ValueCountFrequency (%)
0 83
 
0.3%
2 260
0.9%
3 29
 
0.1%
4 70
 
0.2%
5 156
0.5%
7 27
 
0.1%
9 58
 
0.2%
10 323
1.1%
11 57
 
0.2%
13 100
 
0.3%
ValueCountFrequency (%)
32 78
0.3%
30 68
0.2%
27 134
0.4%
26 67
0.2%
24 55
0.2%
21 120
0.4%
20 90
0.3%
18 48
 
0.2%
17 32
 
0.1%
16 65
0.2%

player_location_area
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1395665
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:54.625553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median8
Q38
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9320025
Coefficient of variation (CV)0.27060501
Kurtosis2.8267331
Mean7.1395665
Median Absolute Deviation (MAD)0
Skewness-1.9048499
Sum215415
Variance3.7326335
MonotonicityNot monotonic
2023-04-20T07:38:54.827765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 16102
53.4%
7 5280
 
17.5%
9 3416
 
11.3%
5 1151
 
3.8%
2 1009
 
3.3%
1 973
 
3.2%
6 901
 
3.0%
3 803
 
2.7%
4 506
 
1.7%
10 31
 
0.1%
ValueCountFrequency (%)
1 973
 
3.2%
2 1009
 
3.3%
3 803
 
2.7%
4 506
 
1.7%
5 1151
 
3.8%
6 901
 
3.0%
7 5280
 
17.5%
8 16102
53.4%
9 3416
 
11.3%
10 31
 
0.1%
ValueCountFrequency (%)
10 31
 
0.1%
9 3416
 
11.3%
8 16102
53.4%
7 5280
 
17.5%
6 901
 
3.0%
5 1151
 
3.8%
4 506
 
1.7%
3 803
 
2.7%
2 1009
 
3.3%
1 973
 
3.2%

player_location_x
Real number (ℝ)

Distinct2143
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.68485
Minimum50.1
Maximum307.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:55.074671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50.1
5-th percentile94.6
Q1125.4
median172
Q3222.1
95-th percentile255.7
Maximum307.3
Range257.2
Interquartile range (IQR)96.7

Descriptive statistics

Standard deviation53.403705
Coefficient of variation (CV)0.30747474
Kurtosis-1.2154154
Mean173.68485
Median Absolute Deviation (MAD)48.2
Skewness0.055065314
Sum5240419.3
Variance2851.9557
MonotonicityNot monotonic
2023-04-20T07:38:55.368318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.2 37
 
0.1%
182.2 36
 
0.1%
123 36
 
0.1%
167.1 35
 
0.1%
104.9 34
 
0.1%
169.4 34
 
0.1%
219.4 33
 
0.1%
182.9 33
 
0.1%
110.1 33
 
0.1%
183.3 33
 
0.1%
Other values (2133) 29828
98.9%
ValueCountFrequency (%)
50.1 1
< 0.1%
52.3 1
< 0.1%
54.4 1
< 0.1%
57 1
< 0.1%
60 1
< 0.1%
62.1 1
< 0.1%
62.4 1
< 0.1%
64.9 1
< 0.1%
65.3 1
< 0.1%
65.4 1
< 0.1%
ValueCountFrequency (%)
307.3 1
< 0.1%
297.8 1
< 0.1%
296.9 1
< 0.1%
296.7 1
< 0.1%
293.6 1
< 0.1%
292.9 1
< 0.1%
290.9 1
< 0.1%
290.1 1
< 0.1%
289.3 1
< 0.1%
289.1 1
< 0.1%

player_location_y
Real number (ℝ)

Distinct22471
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean484.51213
Minimum127.55
Maximum820.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:55.693431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum127.55
5-th percentile192.631
Q1312.34
median530.57
Q3657.0225
95-th percentile768.929
Maximum820.76
Range693.21
Interquartile range (IQR)344.6825

Descriptive statistics

Standard deviation196.12437
Coefficient of variation (CV)0.40478731
Kurtosis-1.4349103
Mean484.51213
Median Absolute Deviation (MAD)177.425
Skewness-0.024857735
Sum14618700
Variance38464.767
MonotonicityNot monotonic
2023-04-20T07:38:56.002044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
613.25 8
 
< 0.1%
632.63 7
 
< 0.1%
639.38 7
 
< 0.1%
626.57 7
 
< 0.1%
287.19 7
 
< 0.1%
638.03 6
 
< 0.1%
774.97 6
 
< 0.1%
360.73 6
 
< 0.1%
758.59 6
 
< 0.1%
348.16 6
 
< 0.1%
Other values (22461) 30106
99.8%
ValueCountFrequency (%)
127.55 1
< 0.1%
135.18 1
< 0.1%
142.2 1
< 0.1%
143.04 1
< 0.1%
143.85 1
< 0.1%
145.33 1
< 0.1%
145.46 2
< 0.1%
147.47 1
< 0.1%
148.22 1
< 0.1%
148.32 1
< 0.1%
ValueCountFrequency (%)
820.76 1
< 0.1%
820.42 1
< 0.1%
818.83 1
< 0.1%
817.93 1
< 0.1%
817.55 2
< 0.1%
817.43 1
< 0.1%
816.89 1
< 0.1%
816.81 1
< 0.1%
814.27 1
< 0.1%
813.39 1
< 0.1%

opponent_location_area
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.865637
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:56.454619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q18
median8
Q38
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75432074
Coefficient of variation (CV)0.095900782
Kurtosis29.605323
Mean7.865637
Median Absolute Deviation (MAD)0
Skewness-4.8246116
Sum237322
Variance0.56899978
MonotonicityNot monotonic
2023-04-20T07:38:56.866980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 26698
88.5%
7 1409
 
4.7%
9 986
 
3.3%
5 443
 
1.5%
6 195
 
0.6%
3 194
 
0.6%
4 122
 
0.4%
1 48
 
0.2%
10 43
 
0.1%
2 34
 
0.1%
ValueCountFrequency (%)
1 48
 
0.2%
2 34
 
0.1%
3 194
 
0.6%
4 122
 
0.4%
5 443
 
1.5%
6 195
 
0.6%
7 1409
 
4.7%
8 26698
88.5%
9 986
 
3.3%
10 43
 
0.1%
ValueCountFrequency (%)
10 43
 
0.1%
9 986
 
3.3%
8 26698
88.5%
7 1409
 
4.7%
6 195
 
0.6%
5 443
 
1.5%
4 122
 
0.4%
3 194
 
0.6%
2 34
 
0.1%
1 48
 
0.2%

opponent_location_x
Real number (ℝ)

Distinct2032
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.5297
Minimum23.7
Maximum316.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:57.351001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum23.7
5-th percentile118.6
Q1146.8
median173.8
Q3201.8
95-th percentile232.5
Maximum316.4
Range292.7
Interquartile range (IQR)55

Descriptive statistics

Standard deviation36.830414
Coefficient of variation (CV)0.21102663
Kurtosis-0.25212598
Mean174.5297
Median Absolute Deviation (MAD)27.5
Skewness0.059891096
Sum5265910.2
Variance1356.4794
MonotonicityNot monotonic
2023-04-20T07:38:57.869888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171.8 47
 
0.2%
169.8 45
 
0.1%
179.7 45
 
0.1%
167.7 44
 
0.1%
175.3 44
 
0.1%
173.2 44
 
0.1%
177.5 43
 
0.1%
178.7 43
 
0.1%
183.5 42
 
0.1%
184.5 41
 
0.1%
Other values (2022) 29734
98.5%
ValueCountFrequency (%)
23.7 1
< 0.1%
26.3 1
< 0.1%
34.9 1
< 0.1%
38.4 1
< 0.1%
41.1 1
< 0.1%
43.5 1
< 0.1%
44.7 1
< 0.1%
47.4 1
< 0.1%
48.4 1
< 0.1%
49.1 1
< 0.1%
ValueCountFrequency (%)
316.4 1
< 0.1%
315.9 1
< 0.1%
314.5 1
< 0.1%
313.2 1
< 0.1%
310 1
< 0.1%
309 1
< 0.1%
308.9 1
< 0.1%
308.5 1
< 0.1%
308.2 1
< 0.1%
307.4 1
< 0.1%

opponent_location_y
Real number (ℝ)

Distinct19786
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean483.59733
Minimum128.79
Maximum851.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:38:58.385838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum128.79
5-th percentile225.7165
Q1293.835
median562.515
Q3670.05
95-th percentile739.6645
Maximum851.68
Range722.89
Interquartile range (IQR)376.215

Descriptive statistics

Standard deviation196.52985
Coefficient of variation (CV)0.40639152
Kurtosis-1.761375
Mean483.59733
Median Absolute Deviation (MAD)192.275
Skewness-0.018735302
Sum14591099
Variance38623.984
MonotonicityNot monotonic
2023-04-20T07:38:58.915805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
287.19 21
 
0.1%
289.85 14
 
< 0.1%
281.83 13
 
< 0.1%
284.51 13
 
< 0.1%
276.41 11
 
< 0.1%
292.5 10
 
< 0.1%
645.08 9
 
< 0.1%
637.69 9
 
< 0.1%
279.13 8
 
< 0.1%
671.83 7
 
< 0.1%
Other values (19776) 30057
99.6%
ValueCountFrequency (%)
128.79 1
< 0.1%
135.07 1
< 0.1%
137.28 1
< 0.1%
137.45 1
< 0.1%
139.45 1
< 0.1%
139.47 1
< 0.1%
143.6 1
< 0.1%
143.66 1
< 0.1%
149.55 1
< 0.1%
149.78 1
< 0.1%
ValueCountFrequency (%)
851.68 1
< 0.1%
829.29 1
< 0.1%
824.63 1
< 0.1%
821.67 1
< 0.1%
820.39 1
< 0.1%
820.07 1
< 0.1%
816.13 1
< 0.1%
814.09 2
< 0.1%
813.98 1
< 0.1%
813.79 1
< 0.1%

set
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size235.8 KiB
1
12667 
2
12609 
3
4896 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30172
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 12667
42.0%
2 12609
41.8%
3 4896
 
16.2%

Length

2023-04-20T07:38:59.430301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-20T07:38:59.917024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 12667
42.0%
2 12609
41.8%
3 4896
 
16.2%

Most occurring characters

ValueCountFrequency (%)
1 12667
42.0%
2 12609
41.8%
3 4896
 
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30172
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12667
42.0%
2 12609
41.8%
3 4896
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
Common 30172
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12667
42.0%
2 12609
41.8%
3 4896
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12667
42.0%
2 12609
41.8%
3 4896
 
16.2%

match_id
Real number (ℝ)

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.168666
Minimum1
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:39:00.363116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q113
median23
Q334
95-th percentile45
Maximum58
Range57
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.168097
Coefficient of variation (CV)0.5448417
Kurtosis-0.81924163
Mean24.168666
Median Absolute Deviation (MAD)10
Skewness0.2212527
Sum729217
Variance173.39878
MonotonicityIncreasing
2023-04-20T07:39:00.678670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
33 1144
 
3.8%
34 1030
 
3.4%
43 992
 
3.3%
15 981
 
3.3%
8 950
 
3.1%
17 950
 
3.1%
13 924
 
3.1%
32 909
 
3.0%
19 894
 
3.0%
37 863
 
2.9%
Other values (34) 20535
68.1%
ValueCountFrequency (%)
1 340
 
1.1%
2 285
 
0.9%
3 503
1.7%
4 704
2.3%
5 284
 
0.9%
6 300
 
1.0%
7 678
2.2%
8 950
3.1%
9 742
2.5%
10 596
2.0%
ValueCountFrequency (%)
58 259
 
0.9%
51 684
2.3%
45 669
2.2%
44 856
2.8%
43 992
3.3%
42 592
2.0%
41 737
2.4%
37 863
2.9%
36 459
1.5%
35 634
2.1%

rally_id
Real number (ℝ)

Distinct2268
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.501
Minimum0
Maximum4938
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:39:00.970097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile348
Q11071.75
median1940
Q32762
95-th percentile3774
Maximum4938
Range4938
Interquartile range (IQR)1690.25

Descriptive statistics

Standard deviation1102.3156
Coefficient of variation (CV)0.55212373
Kurtosis-0.69439751
Mean1996.501
Median Absolute Deviation (MAD)841
Skewness0.28780405
Sum60238429
Variance1215099.7
MonotonicityNot monotonic
2023-04-20T07:39:01.262968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
921 70
 
0.2%
4358 65
 
0.2%
873 61
 
0.2%
895 58
 
0.2%
2587 58
 
0.2%
1148 58
 
0.2%
2463 57
 
0.2%
1996 53
 
0.2%
1827 53
 
0.2%
4936 51
 
0.2%
Other values (2258) 29588
98.1%
ValueCountFrequency (%)
0 8
 
< 0.1%
7 5
 
< 0.1%
8 24
0.1%
10 10
< 0.1%
11 13
< 0.1%
14 7
 
< 0.1%
21 9
 
< 0.1%
22 6
 
< 0.1%
27 11
< 0.1%
28 14
< 0.1%
ValueCountFrequency (%)
4938 6
 
< 0.1%
4936 51
0.2%
4934 22
0.1%
4929 5
 
< 0.1%
4927 7
 
< 0.1%
4920 10
 
< 0.1%
4919 20
 
0.1%
4917 6
 
< 0.1%
4914 7
 
< 0.1%
4912 18
 
0.1%

rally_length
Real number (ℝ)

Distinct52
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.648548
Minimum5
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size235.8 KiB
2023-04-20T07:39:01.546414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q110
median16
Q324
95-th percentile40
Maximum70
Range65
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.2844
Coefficient of variation (CV)0.60510876
Kurtosis2.2565035
Mean18.648548
Median Absolute Deviation (MAD)7
Skewness1.3751665
Sum562664
Variance127.33768
MonotonicityNot monotonic
2023-04-20T07:39:01.820722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 1647
 
5.5%
13 1534
 
5.1%
10 1410
 
4.7%
11 1353
 
4.5%
8 1328
 
4.4%
7 1302
 
4.3%
12 1284
 
4.3%
16 1248
 
4.1%
18 1206
 
4.0%
19 1197
 
4.0%
Other values (42) 16663
55.2%
ValueCountFrequency (%)
5 1155
3.8%
6 1158
3.8%
7 1302
4.3%
8 1328
4.4%
9 1647
5.5%
10 1410
4.7%
11 1353
4.5%
12 1284
4.3%
13 1534
5.1%
14 1120
3.7%
ValueCountFrequency (%)
70 70
0.2%
65 65
 
0.2%
61 61
 
0.2%
58 174
0.6%
57 57
 
0.2%
53 106
0.4%
51 102
0.3%
49 49
 
0.2%
48 48
 
0.2%
47 141
0.5%

Interactions

2023-04-20T07:38:37.460640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:55.279896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:01.627981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:06.472518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:13.005615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:18.677888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:23.376012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:31.280454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:36.059871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:40.774785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:49.073631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:53.761505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:59.270156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:05.224140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:10.095840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:16.414999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:21.098602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:26.543593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:32.818199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:37.698199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:55.523683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:01.876053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:06.713060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:13.391510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:18.908699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:23.673348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:31.612786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:36.296030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:41.020108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:49.312300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:53.987716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:59.662708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:05.488834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:10.343157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:16.654362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:21.352214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:26.950711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:33.051311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:37.947670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:55.803191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:02.145476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:06.973433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:13.785583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:19.159508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:23.942727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:31.861426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:36.549593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:41.295431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:49.571261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:54.230689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:00.077808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:05.758167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:10.593201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:16.902801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:21.626578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:27.326469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:33.294788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:38.215575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:56.050883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:02.415596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:07.239642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:14.181364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:19.412572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:24.208231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:32.120495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:36.801263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:41.564471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:49.818711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:54.490769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:00.497192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:06.018627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:10.842205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:17.150944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:21.883562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:27.665181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:33.551873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:38.473190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:56.283220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:02.666229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:07.484083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:14.570554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:19.653378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:24.461125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:32.358870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:37.038466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:42.411198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:50.064731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:54.735112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:00.877513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:06.265623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:11.075520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:17.397679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:22.129891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:28.068015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:33.784837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:38.717590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:56.526878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:02.926469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:07.733735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:14.973361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:19.880049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:24.718544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:32.600856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:37.293238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:42.777507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:50.302807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:54.987605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:01.230239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:06.515916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:11.323148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:17.655772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:22.379149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:28.372375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:34.034784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:38.974083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:56.784565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:03.182236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:07.992921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:15.394419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:20.136414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:25.110751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:32.844368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:37.542016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:43.914253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:50.572709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:55.237203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:01.575378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:06.779646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:11.629081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:17.912952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:22.652591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:28.736661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:34.283618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:39.223633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:57.068142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:03.435305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:08.244467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:15.755522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:20.357513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:25.999746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:33.075253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:37.774736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:44.532558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:50.806293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:55.462769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:01.796542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:07.023039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:11.989987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:18.156198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:22.901158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:29.122957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:34.515924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:39.466739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:57.409632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:03.679769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:08.499244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:16.004623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:20.606122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:26.537883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:33.321089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:38.017831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:45.330220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:51.054739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:55.715687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:02.046288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:07.288610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:12.299895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:18.406879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:23.152422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:29.530186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:34.758498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:39.732457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:57.784743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:03.947331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:08.758572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:16.251040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:20.857777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:27.004611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:33.571651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:38.297832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:46.347167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:51.321504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:55.974597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:02.307704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:07.551252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:12.692832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:18.671740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:23.420257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:29.935377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:35.020488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:39.982454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:58.167827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:04.197139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:09.014494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:16.509012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:21.099594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:27.596318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:33.828372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:38.554728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:46.733893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:51.567068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:56.204309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:02.558889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:07.806531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:13.086095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:18.911017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:23.677373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:30.277588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:35.265915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:40.234472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:58.542724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:04.440877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:09.262386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:16.728034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:21.322568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:28.049863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:34.078204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:38.785763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:47.015165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:51.805058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:56.429460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:02.794638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:08.050863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:13.455051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:19.137798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:23.917831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:30.680743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:35.491029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:40.471734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:58.918317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:04.694057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:09.513432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:16.964768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:21.569794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:28.390391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:34.324534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:39.029325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:47.277129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:52.042482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:56.670372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:03.051320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:08.312574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:13.846453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:19.375423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:24.164724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:31.093007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:35.730947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:40.733449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:59.323662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:04.966770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:09.768130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:17.211528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:21.830759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:28.798171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:34.579268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:39.294397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:47.551918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:52.288594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:56.972023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:03.742210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:08.579667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:14.215446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:19.641873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:24.428305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:31.353077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:35.987852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:40.959373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:36:59.704388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:05.207919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:10.006125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:17.446495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:22.075905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:29.188362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:34.805275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:39.528947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:47.795562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:52.518332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:57.311718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:03.981687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:08.829883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:14.570461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:19.868900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:24.685861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:31.593859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:36.232013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:41.246468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:00.110253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:05.454097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:10.263678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:17.689281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:22.321869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:29.594455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:35.048181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:39.776303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:48.041174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:52.771219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:57.702547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:04.229249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:09.076340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:14.871485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:20.110669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:24.932950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:31.830528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:36.465613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:41.629109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:00.539016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:05.705796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:10.523083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:17.934021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:22.606318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:30.020300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:35.297128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:40.025715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:48.318223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:53.019798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:58.104761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:04.487635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:09.346966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:15.284991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:20.360135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:25.191055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:32.094800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:36.723139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:41.994719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:00.956525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:05.957656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:10.773652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:18.173836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:22.854922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:30.442622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:35.553216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:40.284877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:48.571609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:53.263973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:58.498444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:04.733849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:09.597611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:15.663583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:20.625261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:25.445663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:32.336602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:36.981010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:42.321061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:01.366137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:06.211632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:11.022108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:18.419247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:23.100583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:30.854360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:35.776954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:40.528284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:48.821963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:53.507606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:37:58.876661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:04.971046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:09.840989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:16.073850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:20.858812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:26.238588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:32.574021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-20T07:38:37.218521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-04-20T07:39:02.112408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
rallyball_roundframe_numroundscore_Aroundscore_Bplayerlanding_arealanding_xlanding_ygetpoint_playerplayer_location_areaplayer_location_xplayer_location_yopponent_location_areaopponent_location_xopponent_location_ymatch_idrally_idrally_lengthtypearoundheadbackhandlanding_heightlose_reasonset
rally1.0000.0140.3340.9500.9390.0200.0100.0010.0020.0470.0050.0070.010-0.0010.0040.005-0.018-0.0080.0250.0120.0150.0000.0050.0000.077
ball_round0.0141.0000.0470.031-0.006-0.007-0.1010.0030.0030.049-0.073-0.0110.006-0.046-0.0020.008-0.027-0.0260.5910.1000.0780.0640.0140.0320.030
frame_num0.3340.0471.0000.3540.2750.0790.013-0.0050.0260.0530.011-0.0060.022-0.009-0.0200.0130.0530.0770.0730.0180.0420.0070.0000.0540.793
roundscore_A0.9500.0310.3541.0000.7880.0240.0020.0040.0040.0380.0030.0070.0120.0010.0040.010-0.014-0.0030.0530.0080.0090.0000.0000.0000.071
roundscore_B0.939-0.0060.2750.7881.0000.0140.016-0.0020.0010.0570.0080.0050.008-0.0030.0040.001-0.021-0.014-0.0060.0080.0240.0000.0000.0000.095
player0.020-0.0070.0790.0240.0141.0000.0080.012-0.0100.543-0.0060.0150.020-0.0060.009-0.0080.4880.488-0.0100.0790.0600.0890.0710.0580.155
landing_area0.010-0.1010.0130.0020.0160.0081.0000.0120.005-0.0040.0680.0240.032-0.0890.008-0.0260.0020.002-0.0820.3650.2490.2880.6900.6360.022
landing_x0.0010.003-0.0050.004-0.0020.0120.0121.000-0.015-0.0040.0230.325-0.0060.0050.172-0.0020.0120.012-0.0070.1300.0730.1560.1270.2720.025
landing_y0.0020.0030.0260.0040.001-0.0100.005-0.0151.000-0.016-0.024-0.045-0.726-0.0010.1340.708-0.010-0.0100.0070.4190.2360.2420.7680.4690.042
getpoint_player0.0470.0490.0530.0380.0570.543-0.004-0.004-0.0161.0000.023-0.0130.035-0.026-0.0240.0090.4720.4710.0490.0750.1360.0411.0000.0380.156
player_location_area0.005-0.0730.0110.0030.008-0.0060.0680.023-0.0240.0231.0000.0850.0240.1310.033-0.009-0.026-0.026-0.0200.2870.2270.4040.1910.0860.016
player_location_x0.007-0.011-0.0060.0070.0050.0150.0240.325-0.045-0.0130.0851.0000.0890.0120.513-0.1060.0040.003-0.0120.1830.1320.2120.0740.0600.027
player_location_y0.0100.0060.0220.0120.0080.0200.032-0.006-0.7260.0350.0240.0891.000-0.015-0.156-0.808-0.010-0.0090.0070.3590.4040.4090.2020.1270.035
opponent_location_area-0.001-0.046-0.0090.001-0.003-0.006-0.0890.005-0.001-0.0260.1310.012-0.0151.0000.0370.012-0.001-0.0020.0080.1720.1000.1420.1580.1250.013
opponent_location_x0.004-0.002-0.0200.0040.0040.0090.0080.1720.134-0.0240.0330.513-0.1560.0371.0000.113-0.008-0.009-0.0070.1800.0910.2140.0610.1110.027
opponent_location_y0.0050.0080.0130.0100.001-0.008-0.026-0.0020.7080.009-0.009-0.106-0.8080.0120.1131.000-0.011-0.0100.0110.2110.1330.1190.1960.1190.056
match_id-0.018-0.0270.053-0.014-0.0210.4880.0020.012-0.0100.472-0.0260.004-0.010-0.001-0.008-0.0111.0001.000-0.0440.0700.0640.0820.0570.0460.158
rally_id-0.008-0.0260.077-0.003-0.0140.4880.0020.012-0.0100.471-0.0260.003-0.009-0.002-0.009-0.0101.0001.000-0.0420.0740.0690.0910.0650.0490.181
rally_length0.0250.5910.0730.053-0.006-0.010-0.082-0.0070.0070.049-0.020-0.0120.0070.008-0.0070.011-0.044-0.0421.0000.0560.0530.0350.0410.0340.087
type0.0120.1000.0180.0080.0080.0790.3650.1300.4190.0750.2870.1830.3590.1720.1800.2110.0700.0740.0561.0000.4750.5040.8250.3920.026
aroundhead0.0150.0780.0420.0090.0240.0600.2490.0730.2360.1360.2270.1320.4040.1000.0910.1330.0640.0690.0530.4751.0000.2500.0300.1320.020
backhand0.0000.0640.0070.0000.0000.0890.2880.1560.2420.0410.4040.2120.4090.1420.2140.1190.0820.0910.0350.5040.2501.0000.0090.2440.017
landing_height0.0050.0140.0000.0000.0000.0710.6900.1270.7681.0000.1910.0740.2020.1580.0610.1960.0570.0650.0410.8250.0300.0091.0001.0000.011
lose_reason0.0000.0320.0540.0000.0000.0580.6360.2720.4690.0380.0860.0600.1270.1250.1110.1190.0460.0490.0340.3920.1320.2441.0001.0000.021
set0.0770.0300.7930.0710.0950.1550.0220.0250.0420.1560.0160.0270.0350.0130.0270.0560.1580.1810.0870.0260.0200.0170.0110.0211.000

Missing values

2023-04-20T07:38:43.030172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-20T07:38:44.401164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-20T07:38:45.239881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

rallyball_roundtimeframe_numroundscore_Aroundscore_Bplayertypearoundheadbackhandlanding_heightlanding_arealanding_xlanding_ylose_reasongetpoint_playerplayer_location_areaplayer_location_xplayer_location_yopponent_location_areaopponent_location_xopponent_location_ysetmatch_idrally_idrally_length
01100:06:0010827100short service0.012.070.7731710.519687NaNNaN8170.8308.958236.7675.211108
11200:06:0110849101net shot0.002.070.086585-0.237240NaNNaN8208.7594.378176.8318.461108
21300:06:0210873100lob0.001.031.4024391.638490NaNNaN7174.2378.288180.1622.541108
31400:06:0310917101clear0.001.041.358537-1.434167NaNNaN3257.9809.088179.3306.431108
41500:06:0410943100drop0.012.011.4878050.359844NaNNaN6273.5268.228233.2738.151108
51600:06:0510966101net shot0.002.021.297561-0.220104NaNNaN1259.8573.618240.6263.581108
61700:06:0610994100lob0.011.09-0.7878051.659635NaNNaN2255.6384.098211.5604.381108
71800:06:0811041101drop0.002.0101.601220-0.504062out0.09128.9789.408154.2292.121108
88100:09:2016805261short service0.012.07-0.280488-0.495677NaNNaN8186.0630.558132.6284.991175
98200:09:2116830260push/rush0.011.031.2048781.544219NaNNaN7125.7391.988166.2633.831175
rallyball_roundtimeframe_numroundscore_Aroundscore_Bplayertypearoundheadbackhandlanding_heightlanding_arealanding_xlanding_ylose_reasongetpoint_playerplayer_location_areaplayer_location_xplayer_location_yopponent_location_areaopponent_location_xopponent_location_ysetmatch_idrally_idrally_length
30162401100:56:0610099319214defensive shot0.012.070.3390240.441719NaNNaN8214.1294.268205.1716.79258491920
30163401200:56:07101018192134net shot0.002.07-0.276829-0.199427NaNNaN8189.8618.398202.3299.32258491920
30164401300:56:0810104419214push/rush0.011.06-0.9560981.489427NaNNaN8162.8356.507184.3573.07258491920
30165401400:56:09101077192134clear1.001.09-0.664634-1.476771NaNNaN8117.9731.188165.6290.03258491920
30166401500:56:1010111919214drop0.002.051.1024390.742865NaNNaN9140.4169.148155.6695.57258491920
30167401600:56:11101142192134defensive shot0.002.070.051220-0.436198NaNNaN8212.5659.348172.5212.66258491920
30168401700:56:1210116719214net shot0.012.070.1451220.290938NaNNaN8162.8349.638189.4647.10258491920
30169401800:56:13101193192134net shot0.002.07-0.097561-0.277240NaNNaN7172.4583.508175.9344.84258491920
30170401900:56:1310121919214lob0.011.09-0.0804881.515417NaNNaN7156.7401.027180.6583.43258491920
30171402000:56:15101272192134smash1.002.061.181707-0.903958opponent's ball landed4.08163.8743.268187.2284.77258491920